StructuralCognition Theory II: Applications, Implementation, and Social Implications
This technical report introduces a cloud-native architecture designed to implement the Cognitive Edge Emergence Model (CEEM). Traditional language model generation relies heavily on generic associations and external valence inferences. In contrast, this architecture establishes an individual-specific, self-reinforcing cognitive framework by decoupling semantic embedding from dynamic edge weight updates. The system utilizes FastAPI on Cloud Run for asynchronous request handling, Vertex AI (Gemini API) strictly for text embeddings without valence inference, and Cloud SQL with pgvector for similarity matching and structural graph persistence. State management dynamically controls cognitive bandwidth B(t) and temperature tau(B), while a dedicated Cloud Run service computes the continuous edge weight evolution via the differential equation: dw_ij / dt = eta * a_i(t) * a_j(t) - lambda * w_ij(t) where edge weights (w_ij) are reinforced exclusively through co-activation (a_i * a_j). This architecture operationalizes a structural profile that mirrors individual cognitive trajectories rather than generic statistical associations. Keywords: Cognitive Edge Emergence Model (CEEM) Cloud Architecture pgvector Valence-free Inference Structural Cognition Lab Neural-Symbolic Integration